KQL MCP Server

The best MCP server for KQL (Kusto Query Language) — supporting Azure Data Explorer, Log Analytics, and Microsoft Sentinel.

Features

Tools

Resources (KQL Reference)

• Tabular operators: where, summarize, join, project, extend, parse, mv-expand, make-series, etc.

• Scalar functions: string, datetime, math, dynamic/JSON, IP address

• Aggregation functions: count, dcount, avg, percentile, make_list, arg_max, etc.

• Window functions: prev, next, row_number, row_cumsum

• Time series functions: series_decompose_anomalies, series_fit_line, series_decompose_forecast

• Data types and timespan literals

• Best practices for performance, readability, and security

Query Templates

• Security: Failed logins, impossible travel, suspicious PowerShell, Azure resource deletions, network anomalies

• Performance: CPU/memory/disk metrics, slow HTTP requests, dependency failures, exception rates

• Operations: Heartbeat health checks, VM events, ingestion volume, alert rule firings

• ADX: Query statistics, ingestion failures, extent stats

• Time Series: Anomaly detection, forecasting, event rate spikes

Prompts

• write-kql — Write a KQL query from a natural language description

• explain-kql — Explain what a query does in plain English

• optimize-kql — Analyze and optimize a query for performance

• investigate-security-alert — Security investigation plan + queries

• performance-investigation — Performance root cause queries

• convert-sql-to-kql — Convert SQL to KQL

• schema-explorer — Explore a table and get query suggestions

Related MCP server: MCP KQL Server

Installation

pip install git+https://github.com/rod-trent/KQL-MCP.git

Or clone and install locally:

git clone https://github.com/rod-trent/KQL-MCP.git
cd KQL-MCP
pip install -e .

Configuration

Copy .env.example to .env and fill in your connection details:

# Azure Data Explorer
ADX_CLUSTERS='[{"name": "my-cluster", "cluster_url": "https://mycluster.eastus.kusto.windows.net", "database": "mydb"}]'

# Log Analytics / Sentinel
LOG_ANALYTICS_WORKSPACES='[{"name": "sentinel", "workspace_id": "your-workspace-id"}]'

# Authentication (cli = az login, managed_identity, service_principal, interactive)
AZURE_AUTH_METHOD=cli

Authentication

The server supports multiple Azure authentication methods:

For cli auth, log in first:

az login

Using with AI Assistants

The KQL MCP server works with any AI assistant or IDE that supports the Model Context Protocol (MCP). Choose your platform below.

Claude Desktop

Config file location:

• Windows: %APPDATA%\Claude\claude_desktop_config.json

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "kql": {
      "command": "kql-mcp",
      "env": {
        "ADX_CLUSTERS": "[{\"name\": \"my-cluster\", \"cluster_url\": \"https://mycluster.eastus.kusto.windows.net\", \"database\": \"mydb\"}]",
        "LOG_ANALYTICS_WORKSPACES": "[{\"name\": \"sentinel\", \"workspace_id\": \"your-workspace-id\"}]",
        "AZURE_AUTH_METHOD": "cli"
      }
    }
  }
}

Alternatively, point to a directory containing your .env file:

{
  "mcpServers": {
    "kql": {
      "command": "kql-mcp",
      "cwd": "C:\\path\\to\\KQL-MCP"
    }
  }
}

Restart Claude Desktop after saving. You should see the KQL tools available in a new conversation.

Claude Code (CLI)

claude mcp add kql -- kql-mcp

To pass connection config directly:

claude mcp add kql \
  -e ADX_CLUSTERS='[{"name":"prod","cluster_url":"https://mycluster.eastus.kusto.windows.net","database":"mydb"}]' \
  -e AZURE_AUTH_METHOD=cli \
  -- kql-mcp

Verify the server is registered:

claude mcp list

ChatGPT (OpenAI)

OpenAI supports MCP servers in the ChatGPT desktop app (macOS and Windows).

Config file location:

• Windows: %APPDATA%\ChatGPT\claude_desktop_config.json

• macOS: ~/Library/Application Support/ChatGPT/claude_desktop_config.json

{
  "mcpServers": {
    "kql": {
      "command": "kql-mcp",
      "env": {
        "ADX_CLUSTERS": "[{\"name\": \"my-cluster\", \"cluster_url\": \"https://mycluster.eastus.kusto.windows.net\", \"database\": \"mydb\"}]",
        "LOG_ANALYTICS_WORKSPACES": "[{\"name\": \"sentinel\", \"workspace_id\": \"your-workspace-id\"}]",
        "AZURE_AUTH_METHOD": "cli"
      }
    }
  }
}

Restart ChatGPT after saving. MCP tools appear automatically when you start a new conversation.

Note: MCP support in ChatGPT desktop requires the latest version of the app. Check OpenAI's documentation for the most current setup instructions.

Cursor

Open Settings → Cursor Settings → MCP and add a new server, or edit ~/.cursor/mcp.json directly:

{
  "mcpServers": {
    "kql": {
      "command": "kql-mcp",
      "env": {
        "ADX_CLUSTERS": "[{\"name\": \"my-cluster\", \"cluster_url\": \"https://mycluster.eastus.kusto.windows.net\", \"database\": \"mydb\"}]",
        "LOG_ANALYTICS_WORKSPACES": "[{\"name\": \"sentinel\", \"workspace_id\": \"your-workspace-id\"}]",
        "AZURE_AUTH_METHOD": "cli"
      }
    }
  }
}

Reload Cursor after saving. The KQL tools will be available to Cursor's AI in Agent mode.

Windsurf (Codeium)

Edit ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "kql": {
      "command": "kql-mcp",
      "env": {
        "ADX_CLUSTERS": "[{\"name\": \"my-cluster\", \"cluster_url\": \"https://mycluster.eastus.kusto.windows.net\", \"database\": \"mydb\"}]",
        "LOG_ANALYTICS_WORKSPACES": "[{\"name\": \"sentinel\", \"workspace_id\": \"your-workspace-id\"}]",
        "AZURE_AUTH_METHOD": "cli"
      }
    }
  }
}

Restart Windsurf after saving. MCP tools are available in Cascade (Windsurf's AI agent).

VS Code (GitHub Copilot)

Add to your VS Code settings.json (open via Ctrl+Shift+P → Preferences: Open User Settings (JSON)):

{
  "mcp": {
    "servers": {
      "kql": {
        "type": "stdio",
        "command": "kql-mcp",
        "env": {
          "ADX_CLUSTERS": "[{\"name\": \"my-cluster\", \"cluster_url\": \"https://mycluster.eastus.kusto.windows.net\", \"database\": \"mydb\"}]",
          "LOG_ANALYTICS_WORKSPACES": "[{\"name\": \"sentinel\", \"workspace_id\": \"your-workspace-id\"}]",
          "AZURE_AUTH_METHOD": "cli"
        }
      }
    }
  }
}

Or add a workspace-scoped .vscode/mcp.json file to share the config with your team:

{
  "servers": {
    "kql": {
      "type": "stdio",
      "command": "kql-mcp",
      "env": {
        "ADX_CLUSTERS": "[{\"name\": \"my-cluster\", \"cluster_url\": \"https://mycluster.eastus.kusto.windows.net\", \"database\": \"mydb\"}]",
        "AZURE_AUTH_METHOD": "cli"
      }
    }
  }
}

The KQL tools are then available in GitHub Copilot Chat when using Agent mode (@agent).

Any Other MCP-Compatible Client

The server speaks standard MCP over stdio. Any client that supports stdio MCP servers can use it with this generic config shape:

{
  "mcpServers": {
    "kql": {
      "command": "kql-mcp",
      "env": {
        "ADX_CLUSTERS": "[{\"name\": \"<alias>\", \"cluster_url\": \"https://<cluster>.<region>.kusto.windows.net\", \"database\": \"<database>\"}]",
        "LOG_ANALYTICS_WORKSPACES": "[{\"name\": \"<alias>\", \"workspace_id\": \"<workspace-id>\"}]",
        "AZURE_AUTH_METHOD": "cli"
      }
    }
  }
}

Key values:

Refer to your AI client's MCP documentation for the exact config file location and format.

Requirements

• Python 3.11+

• Azure CLI (az login) for cli auth mode, or appropriate credentials for other auth methods

• Access to an Azure Data Explorer cluster or Log Analytics / Sentinel workspace